Two New Methods for Network Analysis: Ant Colony Optimization and Reduction by Forgetting

This paper introduces social network analysis as a versatile method with many applications in nursing research. Social networks have been studied for years in many social science fields. The methods continue to advance but remain unknown to most nursing scholars. Discussion paper. English language and interpreted literature was searched from Ovid Healthstar, CINAHL, PubMed Central, Scopus and hard copy texts from 1965 - 2017. Social network analysis first emerged in nursing literature in 1995 and appears minimally through present day. To convey the versatility and applicability of social network analysis in nursing, hypothetical scenarios are presented. The scenarios are illustrative of three approaches to social network analysis and include key elements of social network research design. The methods of social network analysis are underused in nursing research, primarily because they are unknown to most scholars. However, there is methodological flexibility and epistemological versatility capable of supporting quantitative and qualitative research. The analytic techniques of social network analysis can add new insight into many areas of nursing inquiry, especially those influenced by cultural norms. Furthermore, visualization techniques associated with social network analysis can be used to generate new hypotheses. Social network analysis can potentially uncover findings not accessible through methods commonly used in nursing research. Social networks can be analysed based on individual-level attributes, whole networks and subgroups within networks. Computations derived from social network analysis may stand alone to answer a research question or incorporated as variables into robust statistical models.

The k-clique problem is identifying the largest complete subgraph of size k on a network, and it has many applications in Social Network Analysis (SNA), coding theory, geometry, etc. Due to the NP-Complete nature of the problem, the meta-heuristic approaches have raised the interest of the researchers and some algorithms are developed. In this paper, a new algorithm based on the Bat optimization approach is developed for finding the maximum k-clique on a social network to increase the convergence speed and evaluation criteria such as Precision, Recall, and F1-score. The proposed algorithm is simulated in Matlab® software over Dolphin social network and DIMACS dataset for k = 3, 4, 5. The computational results show that the convergence speed on the former dataset is increased in comparison with the Genetic Algorithm (GA) and Ant Colony Optimization (ACO) approaches. Besides, the evaluation criteria are also modified on the latter dataset and the F1-score is obtained as 100% for k = 5.

This article reviews how current social network analysis might be used to investigate individual and group behavior in sporting teams. Social network analysis methods permit researchers to explore social relations between team members and their individual-level qualities simultaneously. As such, social network analysis can be seen as augmenting existing approaches for the examination of intra-group relations among teams and provide detail of team members' informal connections to others within the team. Social network analysis is useful in addressing the issue of interdependencies in the data inherent in team structures. Social network terms are introduced and explained by way of an example team, software and resources are discussed, and a statistical approach to social network analysis is introduced.

Circular economy (CE) is a sustainable alternative to tackle global challenges like climate change, waste, and pollution. The relations, perceptions and behaviors of stakeholders in circular economic activities may form barriers that hinder the circular transition. The promotion of CE requires investigating the interactions and information flow between CE stakeholders from a network perspective. This study revisits the applications of social network analysis (SNA) in promoting CE. Related concepts of CE and the research contents of SNA are reviewed. A bibliometric analysis is conducted to provide a bird’s eye on the research status and trend. On this basis, we summarize the challenges of promoting CE and refine specific problems, around which we review the research status of network modeling methods and statistical measures, information diffusion models, mining methods of perceptions, and social influence analysis. This study outlines the pathways through which SNA contributes to promoting CE, such as through revealing the relational structure and characteristics of stakeholders, forming and changing perceptions of stakeholders, improving behaviors of stakeholders, and examining the development of CE. The lessons learned from the review and future prospects are extensively discussed in combination with the features of the information age from both theoretical and practical perspectives.

This paper discusses the issues of industrial cluster analysis. Initially, the authors explore theoretical approaches to understanding the clusters phenomenon and their identification and analysis. Looking at industrial clusters as network structures connected by various forms of interaction between members, such as ownership linkages, transactions, the presence of common counterparts, and participation in arbitration processes, the authors propose visualizing clusters using social network analysis metrics. This approach helps to address one of the main difficulties when contacting the members of industrial clusters for a subsequent survey or in-depth interviewing. The analysis concludes with a discussion of the proposed method as a way to identify cluster members and determine the most significant ones that are the primary nodes of the network. These key members usually possess enough relevant information about the structure, coordination mechanisms, general strategy, and cluster management system. Therefore, it is possible to limit the list of interviewed respondents without a substantial loss in empirical data quality. The case of the textile industry cluster presented in this paper confirms the applicability of social network analysis to the visualization and description of industrial clusters.

Using social network analysis and agent-based modelling to explore information flow using common operational pictures for maritime search and rescue operations

This article utilizes social network analysis (SNA) and associated statistical techniques to examine a complex process known as Sentence Management in a Scottish Prison Service facility. Findings indicate that communication did not always follow the formal hierarchical process defined by that organization, significant fragmentation existed between certain key roles, and four central actors controlled much of the process. This research demonstrates the applicability of the SNA as an operations management tool to analyse structural communication patterns. This case study further suggests that SNA may be particularly applicable to the public service sector, to better understand informal communication networks within operations that are largely dependent on human performance.

ObjectivesThis study aimed to examine the feasibility of social network analysis as a valuable research tool for indicating a change in research topics in health care and medicine.MethodsPapers used in the analysis were collected from the PubMed database at the National Library of Medicine. After limiting the search to papers affiliated with the National Institutes of Health, 27,125 papers were selected for the analysis. From these papers, the top 100 non-duplicate and most studied Medical Subject Heading terms were extracted. NetMiner V.3 was used for analysis. Weighted degree centrality was applied to the analysis to compare the trends in the change of research topics. Changes in the core keywords were observed for the entire group and in three-year intervals.ResultsThe core keyword with the highest centrality value was "Risk Factor," followed by "Molecular Sequence Data," "Neoplasms," "Signal Transduction," "Brain," and "Amino Acid Sequence." Core keywords varied between time intervals, changing from "Molecular Sequence Data" to "Risk Factors" over time. "Risk Factors" was added as a new keyword and its social network was expanded. The slope of the keywords also varied over time: "Molecular Sequence Data," with a high centrality value, had a decreasing slope at certain intervals, whereas "SNP," with a low centrality value, had an increasing slope at certain intervals.ConclusionsThe social network analysis method is useful for tracking changes in research topics over time. Further research should be conducted to confirm the usefulness of this method in health care and medicine.

With the development of information technology, more and more data about social to be collected. If we can analyze them effectively, it will help people to understand sociological understanding, promoting the development of social science. But the increasing amount of data and analysis to put forward a huge challenge. Now the social networks have already surpassed the processing ability of the original analysis means, must use a more effective tool to complete the analysis task. The computer as a way of helping people from massive data to find the potential useful knowledge tools, play an important role in many fields. Social network analysis, also known as link mining, refers to the handling of the relationship between social network data in the computer method. In this paper, the methods of computer and the social network analysis was introduced in this paper and the computer algorithms are summarized in the application of social network analysis.

The application of social network analysis in marketing can provide marketers with valuable insights for developing communication and branding strategies by building up social capital in social networking sites (SNSs) as Facebook. In this paper, the main characteristics and definitions of social capital and social network analysis (SNA) are reviewed and discussed, including the importance of social capital for marketing and branding in SNSs. SNA is used to analyse the network structure of a famous Greek brand page, and the activity (comments) of its members during a period of two months in 2013. The interactions between consumers, the content generated by the firm and the posts commenting on this content are presented and analysed in terms of SNA and marketing, highlighting the importance of content creation in attracting users to like and comment increasing engagement. The implications and applications of the use of SNA results and of social capital as a way of building brand loyalty in marketing decisions and planning are discussed, and suggestions for further research are offered.

Software engineering is largely concerned with the methodical, systematic production of quality software to support organizational goals. Despite significant advances in technology over the last decade, software engineering still heavily relies on human efforts and human interaction to generate economic contributions for an organization. In more recent years, the question of software service complexity has become central to Global Software Development (GSD) with a view of developing software across a global landscape. However, few efforts have surfaced to challenge the complexity of the relational infrastructure of software teams that support the development of software architecture. This is important in order to sustain and support lean software development and organisational structures particularly in an open service innovation environment. However, from a socio-economic perspective, there are few theoretical efforts that attempt to introduce new insights on how the human factors contribute towards a GSD value co-creation. The objective of this chapter is to examine the application of Social Network Analysis (SNA) and i* (i star) modelling techniques to examine how we could model the economic impact of software relational structures. The chapter explores how i* models leverage SNA concepts to model GSD development. This chapter also offers a discussion on the theoretical development of the socio-economics of GSD in an “open innovation” context.

Social Network Analysis (SNA) is a process of investigating the structure of a network. With the contribution of graph theory, it studies the relationships (links) that are established between different units, such as individuals, groups, or organizations (nodes), which are taken as interdependent rather than as autonomous. A social network can be compared to a web, which records the relationships or interactions of people. These networks include the social or professional interactions between units through which information and services are exchanged or contacts are recorded on a systematic basis. In this paper we attempt to introduce SNA in Economic and Regional Development, i.e. to examine the correlation of a regional education statistic indicator between certain regions. Specifically, we study the share of citizens who have completed higher education for the age group 25-64 years by gender and NUTS 2 regions. We present the visualisation of several networks, which are generated based on the degree of correlation of the data, and compute measures of centrality to compare the level of higher education by gender. The aim is to determine whether the application of Social Network Analysis could be an alternative research method in the study of Economic and Regional Development.

PurposeThe purpose of this study is to explore some quantitative indicators from the social network analysis, observe the variations of these indicators over time and determine whether these indicators fit into the traditional team development stages model or theory. The primary focus is on the perspective of social interaction rather than the suitability of the indicator, i.e. the authors will not determine the optimal indicators nor compare the performance of different indicators. This study aims to propose a quantitative method to identify the development stages of virtual teams.Design/methodology/approachTwo phases were designed in this study. The first phase was a simple study to prove the preliminary ideas and explore which quantitative indicators would be suitable for analysis. In total, 16 undergraduates were randomly assigned to two virtual teams. They were required to complete an information system (IS) project through virtual teamwork and use information and communications technologies (ICTs) to communicate with each other. After proving the preliminary ideas, the authors collected communication data of the 30 virtual teams working on IS projects in the second phase. The total duration of this process was two months.FindingsThe findings practically identified three stages, which were found to be consistent with that of the previous qualitative studies. In the initial (inclusion) stage, intensity had an upward trend. In the second (control) stage, centralization had an upward trend. In the final (affection) stage, intensity and density had upward trends and centralization had a downward trend. Both density and centralization also became smooth in this final stage. The conclusion can serve as a basis for further studies in virtual team development.Originality/valueA successful virtual team will help industries to reduce their costs and increase performance and benefits. The findings will help industries quickly and objectively identify which stage they are at. This quantified approach will provide managers and leaders with a simple, useful way to highlight the needs for managing different aspects of team behavior at each stage of development. After establishing this approach, managers and leaders will be able to make plans to improve existing processes, set priorities, provide suitable principles and guidelines, etc., and eventually improve virtual team performance.

Nighttime lights remote sensing has a significant advantage in exploring the economic development of cities. Based on nighttime lighting data, this study employed spatial direction analysis, exploratory spatial data analysis, and social network analysis to explore the spatial characteristics of economic development and analyzed the economic connection network structures within urban agglomerations in the New Western Land-sea Corridor (NWLSC) in western China. The results show that the spatial pattern of the Tianshan North slope urban agglomeration, Guanzhong Plain urban agglomeration, and Lanzhou–Xining urban agglomeration shrank, while other urban agglomerations expanded. The city economy of the Chengdu–Chongqing urban agglomeration (CCUA) and the Beibu Gulf urban agglomeration varied dramatically according to a LISA space-time transition analysis, which indicates a strong spatial dependence between cities in the local space. Within urban agglomerations, the economic connection between cities increased significantly, and central cities were at the core of the network and significantly influenced other cities. Among the urban agglomerations, economic connections among neighboring urban agglomerations in geographic space increased during the study period. The CCUA gradually developed into the center of the economic network in the NWLSC. Network density positively influenced economic connections. The degree centrality, closeness centrality, and betweenness centrality significantly enhanced the economic connections between city agglomerations. The study’s conclusions and methods can serve as the policy support for the cooperative development of urban agglomerations in NWLSC serve as a guideline for the development of other economically underdeveloped regions in the world.

We propose a novel application of Social Network Analysis (SNA) using classroom video data as a means of quantitatively and visually exploring the collaborations between students. The context for our study was a summer program that works with first generation students and deaf/hard-of-hearing students to engage in authentic science practice and develop a supportive community. We applied SNA to data from one activity during the two-week program to test our approach and as a means to begin to assess whether the goals of the program are being met. We used SNA to identify groups that were interacting in unexpected ways and then to highlight how individuals were contributing to the overall group behavior. We plan to expand our new use of SNA to video data on a larger scale.